Overview

Dataset statistics

Number of variables24
Number of observations30000
Missing cells600
Missing cells (%)0.1%
Duplicate rows32
Duplicate rows (%)0.1%
Total size in memory5.5 MiB
Average record size in memory192.0 B

Variable types

Numeric14
Categorical10

Alerts

Dataset has 32 (0.1%) duplicate rowsDuplicates
bill_statement_sep is highly correlated with bill_statement_aug and 5 other fieldsHigh correlation
bill_statement_aug is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
bill_statement_jul is highly correlated with bill_statement_sep and 6 other fieldsHigh correlation
bill_statement_jun is highly correlated with bill_statement_sep and 9 other fieldsHigh correlation
bill_statement_may is highly correlated with bill_statement_sep and 9 other fieldsHigh correlation
bill_statement_apr is highly correlated with bill_statement_sep and 8 other fieldsHigh correlation
previous_payment_sep is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
previous_payment_aug is highly correlated with bill_statement_jul and 5 other fieldsHigh correlation
previous_payment_jul is highly correlated with bill_statement_jun and 7 other fieldsHigh correlation
previous_payment_jun is highly correlated with bill_statement_jun and 6 other fieldsHigh correlation
previous_payment_may is highly correlated with bill_statement_jun and 5 other fieldsHigh correlation
previous_payment_apr is highly correlated with bill_statement_may and 4 other fieldsHigh correlation
bill_statement_sep is highly correlated with bill_statement_aug and 4 other fieldsHigh correlation
bill_statement_aug is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_jul is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_jun is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_may is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_apr is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_sep is highly correlated with bill_statement_aug and 4 other fieldsHigh correlation
bill_statement_aug is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
bill_statement_jul is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
bill_statement_jun is highly correlated with bill_statement_sep and 4 other fieldsHigh correlation
bill_statement_may is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
bill_statement_apr is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
previous_payment_sep is highly correlated with bill_statement_augHigh correlation
previous_payment_aug is highly correlated with bill_statement_julHigh correlation
previous_payment_jun is highly correlated with bill_statement_mayHigh correlation
previous_payment_may is highly correlated with bill_statement_aprHigh correlation
payment_status_jul is highly correlated with payment_status_jun and 1 other fieldsHigh correlation
payment_status_sep is highly correlated with payment_status_augHigh correlation
payment_status_may is highly correlated with payment_status_jun and 1 other fieldsHigh correlation
payment_status_jun is highly correlated with payment_status_jul and 2 other fieldsHigh correlation
payment_status_aug is highly correlated with payment_status_jul and 1 other fieldsHigh correlation
payment_status_apr is highly correlated with payment_status_may and 1 other fieldsHigh correlation
limit_bal is highly correlated with bill_statement_sep and 5 other fieldsHigh correlation
marriage is highly correlated with ageHigh correlation
age is highly correlated with marriageHigh correlation
payment_status_sep is highly correlated with payment_status_aug and 5 other fieldsHigh correlation
payment_status_aug is highly correlated with payment_status_sep and 4 other fieldsHigh correlation
payment_status_jul is highly correlated with payment_status_sep and 4 other fieldsHigh correlation
payment_status_jun is highly correlated with payment_status_sep and 4 other fieldsHigh correlation
payment_status_may is highly correlated with payment_status_sep and 4 other fieldsHigh correlation
payment_status_apr is highly correlated with payment_status_sep and 4 other fieldsHigh correlation
bill_statement_sep is highly correlated with limit_bal and 6 other fieldsHigh correlation
bill_statement_aug is highly correlated with limit_bal and 6 other fieldsHigh correlation
bill_statement_jul is highly correlated with bill_statement_sep and 6 other fieldsHigh correlation
bill_statement_jun is highly correlated with limit_bal and 6 other fieldsHigh correlation
bill_statement_may is highly correlated with limit_bal and 6 other fieldsHigh correlation
bill_statement_apr is highly correlated with limit_bal and 6 other fieldsHigh correlation
previous_payment_sep is highly correlated with previous_payment_aug and 2 other fieldsHigh correlation
previous_payment_aug is highly correlated with bill_statement_jul and 3 other fieldsHigh correlation
previous_payment_jul is highly correlated with limit_bal and 8 other fieldsHigh correlation
previous_payment_jun is highly correlated with previous_payment_sep and 1 other fieldsHigh correlation
previous_payment_may is highly correlated with bill_statement_jul and 1 other fieldsHigh correlation
default_payment_next_month is highly correlated with payment_status_sepHigh correlation
previous_payment_aug is highly skewed (γ1 = 30.45381745) Skewed
bill_statement_sep has 2008 (6.7%) zeros Zeros
bill_statement_aug has 2506 (8.4%) zeros Zeros
bill_statement_jul has 2870 (9.6%) zeros Zeros
bill_statement_jun has 3195 (10.7%) zeros Zeros
bill_statement_may has 3506 (11.7%) zeros Zeros
bill_statement_apr has 4020 (13.4%) zeros Zeros
previous_payment_sep has 5249 (17.5%) zeros Zeros
previous_payment_aug has 5396 (18.0%) zeros Zeros
previous_payment_jul has 5968 (19.9%) zeros Zeros
previous_payment_jun has 6408 (21.4%) zeros Zeros
previous_payment_may has 6703 (22.3%) zeros Zeros
previous_payment_apr has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2022-05-18 19:57:46.019866
Analysis finished2022-05-18 19:57:59.697253
Duration13.68 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

limit_bal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:57:59.734919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2022-05-18T21:57:59.792997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
Female
18027 
Male
11823 

Length

Max length6
Median length6
Mean length5.207839196
Min length4

Characters and Unicode

Total characters155454
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Female18027
60.1%
Male11823
39.4%
(Missing)150
 
0.5%

Length

2022-05-18T21:57:59.841674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:57:59.884879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
female18027
60.4%
male11823
39.6%

Most occurring characters

ValueCountFrequency (%)
e47877
30.8%
a29850
19.2%
l29850
19.2%
F18027
 
11.6%
m18027
 
11.6%
M11823
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125604
80.8%
Uppercase Letter29850
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47877
38.1%
a29850
23.8%
l29850
23.8%
m18027
 
14.4%
Uppercase Letter
ValueCountFrequency (%)
F18027
60.4%
M11823
39.6%

Most occurring scripts

ValueCountFrequency (%)
Latin155454
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47877
30.8%
a29850
19.2%
l29850
19.2%
F18027
 
11.6%
m18027
 
11.6%
M11823
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII155454
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47877
30.8%
a29850
19.2%
l29850
19.2%
F18027
 
11.6%
m18027
 
11.6%
M11823
 
7.6%

education
Categorical

Distinct4
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
University
13960 
Graduate school
10537 
High school
4886 
Others
 
467

Length

Max length15
Median length11
Mean length11.86609715
Min length6

Characters and Unicode

Total characters354203
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUniversity
2nd rowUniversity
3rd rowUniversity
4th rowUniversity
5th rowUniversity

Common Values

ValueCountFrequency (%)
University13960
46.5%
Graduate school10537
35.1%
High school4886
 
16.3%
Others467
 
1.6%
(Missing)150
 
0.5%

Length

2022-05-18T21:57:59.919590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:57:59.961081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
school15423
34.1%
university13960
30.8%
graduate10537
23.3%
high4886
 
10.8%
others467
 
1.0%

Most occurring characters

ValueCountFrequency (%)
i32806
 
9.3%
o30846
 
8.7%
s29850
 
8.4%
e24964
 
7.0%
r24964
 
7.0%
t24964
 
7.0%
a21074
 
5.9%
h20776
 
5.9%
15423
 
4.4%
l15423
 
4.4%
Other values (11)113113
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter308930
87.2%
Uppercase Letter29850
 
8.4%
Space Separator15423
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i32806
10.6%
o30846
10.0%
s29850
9.7%
e24964
 
8.1%
r24964
 
8.1%
t24964
 
8.1%
a21074
 
6.8%
h20776
 
6.7%
l15423
 
5.0%
c15423
 
5.0%
Other values (6)67840
22.0%
Uppercase Letter
ValueCountFrequency (%)
U13960
46.8%
G10537
35.3%
H4886
 
16.4%
O467
 
1.6%
Space Separator
ValueCountFrequency (%)
15423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin338780
95.6%
Common15423
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i32806
 
9.7%
o30846
 
9.1%
s29850
 
8.8%
e24964
 
7.4%
r24964
 
7.4%
t24964
 
7.4%
a21074
 
6.2%
h20776
 
6.1%
l15423
 
4.6%
c15423
 
4.6%
Other values (10)97690
28.8%
Common
ValueCountFrequency (%)
15423
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII354203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i32806
 
9.3%
o30846
 
8.7%
s29850
 
8.4%
e24964
 
7.0%
r24964
 
7.0%
t24964
 
7.0%
a21074
 
5.9%
h20776
 
5.9%
15423
 
4.4%
l15423
 
4.4%
Other values (11)113113
31.9%

marriage
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing150
Missing (%)0.5%
Memory size234.5 KiB
Single
15891 
Married
13585 
Others
 
374

Length

Max length7
Median length6
Mean length6.455108878
Min length6

Characters and Unicode

Total characters192685
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Single15891
53.0%
Married13585
45.3%
Others374
 
1.2%
(Missing)150
 
0.5%

Length

2022-05-18T21:57:59.999961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.039948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
single15891
53.2%
married13585
45.5%
others374
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e29850
15.5%
i29476
15.3%
r27544
14.3%
S15891
8.2%
n15891
8.2%
g15891
8.2%
l15891
8.2%
M13585
7.1%
a13585
7.1%
d13585
7.1%
Other values (4)1496
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter162835
84.5%
Uppercase Letter29850
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e29850
18.3%
i29476
18.1%
r27544
16.9%
n15891
9.8%
g15891
9.8%
l15891
9.8%
a13585
8.3%
d13585
8.3%
t374
 
0.2%
h374
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S15891
53.2%
M13585
45.5%
O374
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin192685
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e29850
15.5%
i29476
15.3%
r27544
14.3%
S15891
8.2%
n15891
8.2%
g15891
8.2%
l15891
8.2%
M13585
7.1%
a13585
7.1%
d13585
7.1%
Other values (4)1496
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII192685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e29850
15.5%
i29476
15.3%
r27544
14.3%
S15891
8.2%
n15891
8.2%
g15891
8.2%
l15891
8.2%
M13585
7.1%
a13585
7.1%
d13585
7.1%
Other values (4)1496
 
0.8%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing150
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.49011725
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:00.079827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217852014
Coefficient of variation (CV)0.2597301088
Kurtosis0.04642546306
Mean35.49011725
Median Absolute Deviation (MAD)6
Skewness0.7324726269
Sum1059380
Variance84.96879576
MonotonicityNot monotonic
2022-05-18T21:58:00.127312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291602
 
5.3%
271470
 
4.9%
281402
 
4.7%
301388
 
4.6%
261246
 
4.2%
311208
 
4.0%
251180
 
3.9%
321156
 
3.9%
341154
 
3.8%
331139
 
3.8%
Other values (46)16905
56.4%
ValueCountFrequency (%)
2166
 
0.2%
22558
 
1.9%
23922
3.1%
241120
3.7%
251180
3.9%
261246
4.2%
271470
4.9%
281402
4.7%
291602
5.3%
301388
4.6%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

payment_status_sep
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
17496 
Payed duly
5686 
Payment delayed 1 month
3688 
Payment delayed 2 months
2667 
Payment delayed 3 months
 
322
Other values (5)
 
141

Length

Max length24
Median length7
Mean length11.3092
Min length7

Characters and Unicode

Total characters339276
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayment delayed 2 months
2nd rowPayed duly
3rd rowUnknown
4th rowUnknown
5th rowPayed duly

Common Values

ValueCountFrequency (%)
Unknown17496
58.3%
Payed duly5686
 
19.0%
Payment delayed 1 month3688
 
12.3%
Payment delayed 2 months2667
 
8.9%
Payment delayed 3 months322
 
1.1%
Payment delayed 4 months76
 
0.3%
Payment delayed 5 months26
 
0.1%
Payment delayed 8 months19
 
0.1%
Payment delayed 6 months11
 
< 0.1%
Payment delayed 7 months9
 
< 0.1%

Length

2022-05-18T21:58:00.172561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.220515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown17496
31.2%
payment6818
 
12.1%
delayed6818
 
12.1%
payed5686
 
10.1%
duly5686
 
10.1%
13688
 
6.6%
month3688
 
6.6%
months3130
 
5.6%
22667
 
4.8%
3322
 
0.6%
Other values (5)141
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n66124
19.5%
e26140
 
7.7%
26140
 
7.7%
y25008
 
7.4%
d25008
 
7.4%
o24314
 
7.2%
a19322
 
5.7%
U17496
 
5.2%
k17496
 
5.2%
w17496
 
5.2%
Other values (15)74732
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter276318
81.4%
Uppercase Letter30000
 
8.8%
Space Separator26140
 
7.7%
Decimal Number6818
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n66124
23.9%
e26140
 
9.5%
y25008
 
9.1%
d25008
 
9.1%
o24314
 
8.8%
a19322
 
7.0%
k17496
 
6.3%
w17496
 
6.3%
t13636
 
4.9%
m13636
 
4.9%
Other values (4)28138
10.2%
Decimal Number
ValueCountFrequency (%)
13688
54.1%
22667
39.1%
3322
 
4.7%
476
 
1.1%
526
 
0.4%
819
 
0.3%
611
 
0.2%
79
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U17496
58.3%
P12504
41.7%
Space Separator
ValueCountFrequency (%)
26140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin306318
90.3%
Common32958
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n66124
21.6%
e26140
 
8.5%
y25008
 
8.2%
d25008
 
8.2%
o24314
 
7.9%
a19322
 
6.3%
U17496
 
5.7%
k17496
 
5.7%
w17496
 
5.7%
t13636
 
4.5%
Other values (6)54278
17.7%
Common
ValueCountFrequency (%)
26140
79.3%
13688
 
11.2%
22667
 
8.1%
3322
 
1.0%
476
 
0.2%
526
 
0.1%
819
 
0.1%
611
 
< 0.1%
79
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII339276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n66124
19.5%
e26140
 
7.7%
26140
 
7.7%
y25008
 
7.4%
d25008
 
7.4%
o24314
 
7.2%
a19322
 
5.7%
U17496
 
5.2%
k17496
 
5.2%
w17496
 
5.2%
Other values (15)74732
22.0%

payment_status_aug
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
19512 
Payed duly
6050 
Payment delayed 2 months
3927 
Payment delayed 3 months
 
326
Payment delayed 4 months
 
99
Other values (5)
 
86

Length

Max length24
Median length7
Mean length10.11893333
Min length7

Characters and Unicode

Total characters303568
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPayment delayed 2 months
2nd rowPayment delayed 2 months
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown19512
65.0%
Payed duly6050
 
20.2%
Payment delayed 2 months3927
 
13.1%
Payment delayed 3 months326
 
1.1%
Payment delayed 4 months99
 
0.3%
Payment delayed 1 month28
 
0.1%
Payment delayed 5 months25
 
0.1%
Payment delayed 7 months20
 
0.1%
Payment delayed 6 months12
 
< 0.1%
Payment delayed 8 months1
 
< 0.1%

Length

2022-05-18T21:58:00.273551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.322157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown19512
39.5%
payed6050
 
12.3%
duly6050
 
12.3%
payment4438
 
9.0%
delayed4438
 
9.0%
months4410
 
8.9%
23927
 
8.0%
3326
 
0.7%
499
 
0.2%
128
 
0.1%
Other values (5)86
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n67412
22.2%
o23950
 
7.9%
y20976
 
6.9%
d20976
 
6.9%
U19512
 
6.4%
k19512
 
6.4%
w19512
 
6.4%
19364
 
6.4%
e19364
 
6.4%
a14926
 
4.9%
Other values (15)58064
19.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter249766
82.3%
Uppercase Letter30000
 
9.9%
Space Separator19364
 
6.4%
Decimal Number4438
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n67412
27.0%
o23950
 
9.6%
y20976
 
8.4%
d20976
 
8.4%
k19512
 
7.8%
w19512
 
7.8%
e19364
 
7.8%
a14926
 
6.0%
l10488
 
4.2%
m8876
 
3.6%
Other values (4)23774
 
9.5%
Decimal Number
ValueCountFrequency (%)
23927
88.5%
3326
 
7.3%
499
 
2.2%
128
 
0.6%
525
 
0.6%
720
 
0.5%
612
 
0.3%
81
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
U19512
65.0%
P10488
35.0%
Space Separator
ValueCountFrequency (%)
19364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin279766
92.2%
Common23802
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n67412
24.1%
o23950
 
8.6%
y20976
 
7.5%
d20976
 
7.5%
U19512
 
7.0%
k19512
 
7.0%
w19512
 
7.0%
e19364
 
6.9%
a14926
 
5.3%
l10488
 
3.7%
Other values (6)43138
15.4%
Common
ValueCountFrequency (%)
19364
81.4%
23927
 
16.5%
3326
 
1.4%
499
 
0.4%
128
 
0.1%
525
 
0.1%
720
 
0.1%
612
 
0.1%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII303568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n67412
22.2%
o23950
 
7.9%
y20976
 
6.9%
d20976
 
6.9%
U19512
 
6.4%
k19512
 
6.4%
w19512
 
6.4%
19364
 
6.4%
e19364
 
6.4%
a14926
 
4.9%
Other values (15)58064
19.1%

payment_status_jul
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
19849 
Payed duly
5938 
Payment delayed 2 months
3819 
Payment delayed 3 months
 
240
Payment delayed 4 months
 
76
Other values (5)
 
78

Length

Max length24
Median length7
Mean length9.981033333
Min length7

Characters and Unicode

Total characters299431
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayed duly
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowPayed duly

Common Values

ValueCountFrequency (%)
Unknown19849
66.2%
Payed duly5938
 
19.8%
Payment delayed 2 months3819
 
12.7%
Payment delayed 3 months240
 
0.8%
Payment delayed 4 months76
 
0.3%
Payment delayed 7 months27
 
0.1%
Payment delayed 6 months23
 
0.1%
Payment delayed 5 months21
 
0.1%
Payment delayed 1 month4
 
< 0.1%
Payment delayed 8 months3
 
< 0.1%

Length

2022-05-18T21:58:00.373828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.421961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown19849
40.9%
payed5938
 
12.2%
duly5938
 
12.2%
payment4213
 
8.7%
delayed4213
 
8.7%
months4209
 
8.7%
23819
 
7.9%
3240
 
0.5%
476
 
0.2%
727
 
0.1%
Other values (5)55
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n67973
22.7%
o24062
 
8.0%
y20302
 
6.8%
d20302
 
6.8%
U19849
 
6.6%
k19849
 
6.6%
w19849
 
6.6%
18577
 
6.2%
e18577
 
6.2%
a14364
 
4.8%
Other values (15)55727
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter246641
82.4%
Uppercase Letter30000
 
10.0%
Space Separator18577
 
6.2%
Decimal Number4213
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n67973
27.6%
o24062
 
9.8%
y20302
 
8.2%
d20302
 
8.2%
k19849
 
8.0%
w19849
 
8.0%
e18577
 
7.5%
a14364
 
5.8%
l10151
 
4.1%
m8426
 
3.4%
Other values (4)22786
 
9.2%
Decimal Number
ValueCountFrequency (%)
23819
90.6%
3240
 
5.7%
476
 
1.8%
727
 
0.6%
623
 
0.5%
521
 
0.5%
14
 
0.1%
83
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U19849
66.2%
P10151
33.8%
Space Separator
ValueCountFrequency (%)
18577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin276641
92.4%
Common22790
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n67973
24.6%
o24062
 
8.7%
y20302
 
7.3%
d20302
 
7.3%
U19849
 
7.2%
k19849
 
7.2%
w19849
 
7.2%
e18577
 
6.7%
a14364
 
5.2%
l10151
 
3.7%
Other values (6)41363
15.0%
Common
ValueCountFrequency (%)
18577
81.5%
23819
 
16.8%
3240
 
1.1%
476
 
0.3%
727
 
0.1%
623
 
0.1%
521
 
0.1%
14
 
< 0.1%
83
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII299431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n67973
22.7%
o24062
 
8.0%
y20302
 
6.8%
d20302
 
6.8%
U19849
 
6.6%
k19849
 
6.6%
w19849
 
6.6%
18577
 
6.2%
e18577
 
6.2%
a14364
 
4.8%
Other values (15)55727
18.6%

payment_status_jun
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
20803 
Payed duly
5687 
Payment delayed 2 months
3159 
Payment delayed 3 months
 
180
Payment delayed 4 months
 
69
Other values (5)
 
102

Length

Max length24
Median length7
Mean length9.557633333
Min length7

Characters and Unicode

Total characters286729
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayed duly
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown20803
69.3%
Payed duly5687
 
19.0%
Payment delayed 2 months3159
 
10.5%
Payment delayed 3 months180
 
0.6%
Payment delayed 4 months69
 
0.2%
Payment delayed 7 months58
 
0.2%
Payment delayed 5 months35
 
0.1%
Payment delayed 6 months5
 
< 0.1%
Payment delayed 8 months2
 
< 0.1%
Payment delayed 1 month2
 
< 0.1%

Length

2022-05-18T21:58:00.473624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.522125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown20803
45.0%
payed5687
 
12.3%
duly5687
 
12.3%
payment3510
 
7.6%
delayed3510
 
7.6%
months3508
 
7.6%
23159
 
6.8%
3180
 
0.4%
469
 
0.1%
758
 
0.1%
Other values (5)46
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n69429
24.2%
o24313
 
8.5%
U20803
 
7.3%
k20803
 
7.3%
w20803
 
7.3%
y18394
 
6.4%
d18394
 
6.4%
16217
 
5.7%
e16217
 
5.7%
a12707
 
4.4%
Other values (15)48649
17.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter237002
82.7%
Uppercase Letter30000
 
10.5%
Space Separator16217
 
5.7%
Decimal Number3510
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n69429
29.3%
o24313
 
10.3%
k20803
 
8.8%
w20803
 
8.8%
y18394
 
7.8%
d18394
 
7.8%
e16217
 
6.8%
a12707
 
5.4%
l9197
 
3.9%
m7020
 
3.0%
Other values (4)19725
 
8.3%
Decimal Number
ValueCountFrequency (%)
23159
90.0%
3180
 
5.1%
469
 
2.0%
758
 
1.7%
535
 
1.0%
65
 
0.1%
82
 
0.1%
12
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U20803
69.3%
P9197
30.7%
Space Separator
ValueCountFrequency (%)
16217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin267002
93.1%
Common19727
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n69429
26.0%
o24313
 
9.1%
U20803
 
7.8%
k20803
 
7.8%
w20803
 
7.8%
y18394
 
6.9%
d18394
 
6.9%
e16217
 
6.1%
a12707
 
4.8%
l9197
 
3.4%
Other values (6)35942
13.5%
Common
ValueCountFrequency (%)
16217
82.2%
23159
 
16.0%
3180
 
0.9%
469
 
0.3%
758
 
0.3%
535
 
0.2%
65
 
< 0.1%
82
 
< 0.1%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII286729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n69429
24.2%
o24313
 
8.5%
U20803
 
7.3%
k20803
 
7.3%
w20803
 
7.3%
y18394
 
6.4%
d18394
 
6.4%
16217
 
5.7%
e16217
 
5.7%
a12707
 
4.4%
Other values (15)48649
17.0%

payment_status_may
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
21493 
Payed duly
5539 
Payment delayed 2 months
2626 
Payment delayed 3 months
 
178
Payment delayed 4 months
 
84
Other values (4)
 
80

Length

Max length24
Median length7
Mean length9.235766667
Min length7

Characters and Unicode

Total characters277073
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown21493
71.6%
Payed duly5539
 
18.5%
Payment delayed 2 months2626
 
8.8%
Payment delayed 3 months178
 
0.6%
Payment delayed 4 months84
 
0.3%
Payment delayed 7 months58
 
0.2%
Payment delayed 5 months17
 
0.1%
Payment delayed 6 months4
 
< 0.1%
Payment delayed 8 months1
 
< 0.1%

Length

2022-05-18T21:58:00.574995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.622050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown21493
48.4%
payed5539
 
12.5%
duly5539
 
12.5%
payment2968
 
6.7%
delayed2968
 
6.7%
months2968
 
6.7%
22626
 
5.9%
3178
 
0.4%
484
 
0.2%
758
 
0.1%
Other values (3)22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n70415
25.4%
o24461
 
8.8%
U21493
 
7.8%
k21493
 
7.8%
w21493
 
7.8%
y17014
 
6.1%
d17014
 
6.1%
14443
 
5.2%
e14443
 
5.2%
a11475
 
4.1%
Other values (14)43329
15.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter229662
82.9%
Uppercase Letter30000
 
10.8%
Space Separator14443
 
5.2%
Decimal Number2968
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n70415
30.7%
o24461
 
10.7%
k21493
 
9.4%
w21493
 
9.4%
y17014
 
7.4%
d17014
 
7.4%
e14443
 
6.3%
a11475
 
5.0%
l8507
 
3.7%
m5936
 
2.6%
Other values (4)17411
 
7.6%
Decimal Number
ValueCountFrequency (%)
22626
88.5%
3178
 
6.0%
484
 
2.8%
758
 
2.0%
517
 
0.6%
64
 
0.1%
81
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
U21493
71.6%
P8507
 
28.4%
Space Separator
ValueCountFrequency (%)
14443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin259662
93.7%
Common17411
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n70415
27.1%
o24461
 
9.4%
U21493
 
8.3%
k21493
 
8.3%
w21493
 
8.3%
y17014
 
6.6%
d17014
 
6.6%
e14443
 
5.6%
a11475
 
4.4%
l8507
 
3.3%
Other values (6)31854
12.3%
Common
ValueCountFrequency (%)
14443
83.0%
22626
 
15.1%
3178
 
1.0%
484
 
0.5%
758
 
0.3%
517
 
0.1%
64
 
< 0.1%
81
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII277073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n70415
25.4%
o24461
 
8.8%
U21493
 
7.8%
k21493
 
7.8%
w21493
 
7.8%
y17014
 
6.1%
d17014
 
6.1%
14443
 
5.2%
e14443
 
5.2%
a11475
 
4.1%
Other values (14)43329
15.6%

payment_status_apr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
Unknown
21181 
Payed duly
5740 
Payment delayed 2 months
2766 
Payment delayed 3 months
 
184
Payment delayed 4 months
 
49
Other values (4)
 
80

Length

Max length24
Median length7
Mean length9.318766667
Min length7

Characters and Unicode

Total characters279563
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowPayment delayed 2 months
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown21181
70.6%
Payed duly5740
 
19.1%
Payment delayed 2 months2766
 
9.2%
Payment delayed 3 months184
 
0.6%
Payment delayed 4 months49
 
0.2%
Payment delayed 7 months46
 
0.2%
Payment delayed 6 months19
 
0.1%
Payment delayed 5 months13
 
< 0.1%
Payment delayed 8 months2
 
< 0.1%

Length

2022-05-18T21:58:00.671868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:00.718503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown21181
47.1%
payed5740
 
12.8%
duly5740
 
12.8%
payment3079
 
6.8%
delayed3079
 
6.8%
months3079
 
6.8%
22766
 
6.1%
3184
 
0.4%
449
 
0.1%
746
 
0.1%
Other values (3)34
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n69701
24.9%
o24260
 
8.7%
U21181
 
7.6%
k21181
 
7.6%
w21181
 
7.6%
y17638
 
6.3%
d17638
 
6.3%
14977
 
5.4%
e14977
 
5.4%
a11898
 
4.3%
Other values (14)44931
16.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter231507
82.8%
Uppercase Letter30000
 
10.7%
Space Separator14977
 
5.4%
Decimal Number3079
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n69701
30.1%
o24260
 
10.5%
k21181
 
9.1%
w21181
 
9.1%
y17638
 
7.6%
d17638
 
7.6%
e14977
 
6.5%
a11898
 
5.1%
l8819
 
3.8%
m6158
 
2.7%
Other values (4)18056
 
7.8%
Decimal Number
ValueCountFrequency (%)
22766
89.8%
3184
 
6.0%
449
 
1.6%
746
 
1.5%
619
 
0.6%
513
 
0.4%
82
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U21181
70.6%
P8819
29.4%
Space Separator
ValueCountFrequency (%)
14977
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin261507
93.5%
Common18056
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n69701
26.7%
o24260
 
9.3%
U21181
 
8.1%
k21181
 
8.1%
w21181
 
8.1%
y17638
 
6.7%
d17638
 
6.7%
e14977
 
5.7%
a11898
 
4.5%
l8819
 
3.4%
Other values (6)33033
12.6%
Common
ValueCountFrequency (%)
14977
82.9%
22766
 
15.3%
3184
 
1.0%
449
 
0.3%
746
 
0.3%
619
 
0.1%
513
 
0.1%
82
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII279563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n69701
24.9%
o24260
 
8.7%
U21181
 
7.6%
k21181
 
7.6%
w21181
 
7.6%
y17638
 
6.3%
d17638
 
6.3%
14977
 
5.4%
e14977
 
5.4%
a11898
 
4.3%
Other values (14)44931
16.1%

bill_statement_sep
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2022-05-18T21:58:00.774818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2022-05-18T21:58:00.824799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
50025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

bill_statement_aug
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2022-05-18T21:58:00.876490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2022-05-18T21:58:00.929913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
32675
 
0.2%
78075
 
0.2%
31672
 
0.2%
39651
 
0.2%
250051
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

bill_statement_jul
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2022-05-18T21:58:00.980246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2022-05-18T21:58:01.030557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

bill_statement_jun
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2022-05-18T21:58:01.083335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2022-05-18T21:58:01.134457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
240039
 
0.1%
15039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

bill_statement_may
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2022-05-18T21:58:01.185021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2022-05-18T21:58:01.238560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

bill_statement_apr
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2022-05-18T21:58:01.287667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2022-05-18T21:58:01.340549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

previous_payment_sep
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:01.393350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2022-05-18T21:58:01.440114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

previous_payment_aug
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:01.488407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2022-05-18T21:58:01.537822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

previous_payment_jul
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:01.590478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2022-05-18T21:58:01.637097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

previous_payment_jun
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:01.684949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2022-05-18T21:58:01.731680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

previous_payment_may
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:01.782240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2022-05-18T21:58:02.020908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

previous_payment_apr
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2022-05-18T21:58:02.069495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2022-05-18T21:58:02.120919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

default_payment_next_month
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Length

2022-05-18T21:58:02.164499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-18T21:58:02.201892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number30000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common30000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
023364
77.9%
16636
 
22.1%

Interactions

2022-05-18T21:57:58.372720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:49.178663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.882170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.693904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.407966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.138397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.381400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.058093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.761073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.430080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.264989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.991281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.702498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.421091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.434220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.233661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.932794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.745961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.460212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.186759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.431461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.107981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.808462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:57.044546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:48.482117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:50.795497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.511538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.236165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.479127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.159244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.856878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.534318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.359682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.097737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.798911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:48.533597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:51.564898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.289518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:54.210024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:57.150533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.846535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.566802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.582207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.386444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.091305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:57.200537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:53.629711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.312404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.004459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.808303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:58.666309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.725975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.489196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.193267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:52.441613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.675884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:55.051271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.860645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.554309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.301449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.991462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.711902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.774238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.535710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:53.722449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.409753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.096840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.908406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.600428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.347706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.036298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.762891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:49.586943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.296087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.103636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.830763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.083846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.772784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.463509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.148205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.961066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.649459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.400683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.087954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.809046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.873678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-05-18T21:57:51.152840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.880820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.130602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.818488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.511516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.193077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.009117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.695620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.447199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.133452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.995836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:48.958729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.686881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.403668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.207148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.934440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.182734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.869936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.564811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.242760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.064348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.745502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.499236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.183675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:59.041069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.023590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.734664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.470399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.256819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.985884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.232304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.915155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.612119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.288945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.111305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.790333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.550467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.232108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:59.091531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.079491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.785562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.544421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.309724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.039488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.284009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.964050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.662663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.336718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.163517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.852002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.603666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.279690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:59.137442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.126544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:49.833192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:50.631982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:51.357192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:52.088807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:53.333304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.010658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:54.711441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:55.381069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.211595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:56.919391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:57.654348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-18T21:57:58.324855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-18T21:58:02.238637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-18T21:58:02.319370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-18T21:58:02.400240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-18T21:58:02.482219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-18T21:58:02.552397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-18T21:57:59.229164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-18T21:57:59.444307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-18T21:57:59.567151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-18T21:57:59.618532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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2999950000MaleUniversityMarried46.0UnknownUnknownUnknownUnknownUnknownUnknown4792948905497643653532428153132078180014301000100010001

Duplicate rows

Most frequently occurring

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